GIS and Remote Sensing Tools, Tips and more ..

The second annual technical UAS symposium sponsored by the American Society for Photogrammetry and Remote Sensing (ASPRS) is scheduled for September 29-30, 2015 in Reno, Nevada. Expanding on the highly successful format and events of last year’s symposium this year’s event will include test flights, UAS data processing, and workshops. Technical Demonstration and Symposium for Unmanned Aircraft Systems hosted by the American Society for Photogrammetry and Remote Sensing Call for Speakers and Call for Workshops has also been announced! Details:  http://uasreno.org/   Purpose:  To assemble UAS developers and researchers, along with geospatial service providers and users of geospatial map data, to share information, showcase new technologies and demonstrate UAS systems in action (in flight).  Mission:  To advance knowledge and improve the understanding of UAS technologies and their safe and efficient introduction into our national airspace,...

As the global leader in satellite imagery, DigitalGlobe is proud to once again push the boundaries of innovation by being the first company to delivery 30 cm resolution imagery. This 5x improvement in resolution represents the definition of very-high resolution imagery. 30 cm imagery delivers clearer, richer images that empower better decision making through improved situational awareness. - See more at: Digital Globe 30 cm resolution imagery

A recent study conducted by Yanhua Zhuang et al. (2013) reported   the top 20 research institutes in remote sensing using bibliometric analysis on publications published during 1991-2010 in the remote sensing field.  Oho, Satellites are making RS research interesting!

Satellite remote sensing provides valuable information that can be used to map infectious diseases and forecast future health risks. However, amassing and managing the geographic information from diverse datasets is difficult and time-consuming. Therefore, there is a need for a geoinformatics system that integrates the acquisition, processing, management, and analysis of geospatial data sets from various sources. Here, we present our software model for automated data capture and processing of satellite remote sensing data for public health applications.  The system incorporates land surface temperature and vegetation indices from MODIS precipitation data from TRMM, and a novel measurement of actual evapotranspiration.

Books Pro Cons It provides solid guide to how geospatial analysis work, particularly with respect to GIS. The book emphasizes conceptual workflows and with basic math which is helpful for creating own code and also getting an understanding of what's happening under the hood in contemporary GIS. It is better to have an update because lots of changes in GIS software over last five years. This book is for typical GIS user aspiring to design good maps. It is illustrating GIS map software and throughout with map samples in color which is especially useful for those who has little prior training or experience in map making. This is acceptable book for beginners but very little information of advanced users. It hardly touches on advanced cartographic representations. This book explains the computational geometry and algorithms concisely and very readable. It emphasis on describing algori...

Time lapse sequences of photographs taken with a special low-light 4K-camera (made in Japan)  by the crew of expedition 28 & 29 onboard the International Space Station from  August to October, 2011. Shooting locations in order of appearance: 1. Aurora Borealis Pass over the United States at Night 2. Aurora Borealis and eastern United States at Night 3. Aurora Australis from Madagascar to southwest of Australia 4. Aurora Australis south of Australia 5. Northwest coast of United States to Central South America at Night 6. Aurora Australis from the Southern to the Northern Pacific Ocean 7. Halfway around the World 8. Night Pass over Central Africa and the Middle East 9. Evening Pass over the Sahara Desert and the Middle East 10. Pass over Canada and Central United States at Night 11. Pass over Southern California to Hudson Bay 12. Islands in the Philippine Sea at Night 13. Pass over Eastern Asia to Philippine Sea and Guam 14. Views of the Mideast at Night 15....

The U.S. experienced unprecedented tornado activity throughout the month of April 2011. The NOAA Storm Prediction Center received 875 tornado reports during that month alone; 625 have been confirmed as tornadoes, so far. Many of these storms were concentrated during 7 different major outbreaks, mostly in the Southern U.S. The largest of these outbreaks occurred during April 27-28, leaving over 300 people dead as over 180 storms were reported from Texas to Virginia. This animation shows the GOES-East infrared imagery from April 1-30, along with the locations of each tornado that formed during the time (symbolized as red dots). Though tornadoes cannot actually be seen by GOES, these satellites are instrumental in being able to detect the conditions associated with their formation. As the resolution of GOES has increased with each successive satellite series, so have the warning times for tornadoes. The future GOES-R satellite will provide even higher resolution and storm pre...

NOAA releases the "before" and "after" shots for the damage caused by last week's F5 tornado in McFarland Boulevard in Tuscaloosa. Those images are captured from 5,000 ft high using special remote sensing equipment.

# This is a Python script that automatically downloads historical # MODIS data from the LP DAAC FTP site # This version should work for all of the tiled datasets # It is currently hard-coded to downloaded specific MODIS tiles for # the northern Great Plains & upper midwest # Initailly historical date for Data transfer must be set on "lpdacc.txt". import os, ftplib,sys,string # Login information for accessing the LP DAAC FTP site Hostname = "e4ftl01u.ecs.nasa.gov" Username = "anonymous" Password = "@anonymous" # Get user inputs # Base directory for the MODIS data # Basedir = input("Enter the LP DAAC directory containing the dataset you want to download:") Basedir="MOLT/MOD11A2.005" print "The LP DAAC directory containing the dataset you want to download" +str(Basedir) # Local directory for data storage #Hdfdir = input("Enter the local directory where you w...